Name
Abstract | ||
|---|---|---|
Sequencing studies have been discovering a numerous number of rare variants, allowing the identification of the effects of
rare variants on disease susceptibility. As a method to increase the statistical power of studies on rare variants, several
groupwise association tests that group rare variants in genes and detect associations between groups and diseases have been
proposed. One major challenge in these methods is to determine which variants are causal in a group, and to overcome this
challenge, previous methods used prior information that specifies how likely each variant is causal. Another source of information
that can be used to determine causal variants is observation data because case individuals are likely to have more causal
variants than control individuals. In this paper, we introduce a likelihood ratio test (LRT) that uses both data and prior
information to infer which variants are causal and uses this finding to determine whether a group of variants is involved
in a disease. We demonstrate through simulations that LRT achieves higher power than previous methods. We also evaluate our
method on mutation screening data of the susceptibility gene for ataxia telangiectasia, and show that LRT can detect an association
in real data. To increase the computational speed of our method, we show how we can decompose the computation of LRT, and
propose an efficient permutation test. With this optimization, we can efficiently compute an LRT statistic and its significance
at a genome-wide level. The software for our method is publicly available at http://genetics.cs.ucla.edu/rarevariants.
|
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-20036-6_41 | Journal of computational biology : a journal of computational molecular cell biology |
Keywords | Field | DocType |
rare variant,group rare variant,causal variant,previous method,groupwise association test,lrt statistic,likelihood ratio test,observation data,mutation screening data,prior information,disease susceptibility,permutation test,statistical power,genetics | Association tests,Statistic,Likelihood-ratio test,Computer science,Genetic association,Single-nucleotide polymorphism,Bioinformatics,Minor allele frequency,Genetics,Statistical power,Resampling | Journal |
Volume | Issue | ISSN |
18 | 11 | 1557-8666 |
Citations | PageRank | References |
4 | 1.00 | 1 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae Hoon Sul | 1 | 22 | 3.07 |
| Buhm Han | 2 | 50 | 8.89 |
| Eleazar Eskin | 3 | 1790 | 170.53 |